A brief survey of tools for genomic regions enrichment analysis
Functional enrichment analysis or pathway enrichment analysis (PEA) is a bioinformatics technique which identifies the most over-represented biological pathways in a list of genes compared to those that would be associated with them by chance. These biological functions are found on bioinformatics a...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Bioinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2022.968327/full |
| _version_ | 1811197647597338624 |
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| author | Davide Chicco Giuseppe Jurman |
| author_facet | Davide Chicco Giuseppe Jurman |
| author_sort | Davide Chicco |
| collection | DOAJ |
| description | Functional enrichment analysis or pathway enrichment analysis (PEA) is a bioinformatics technique which identifies the most over-represented biological pathways in a list of genes compared to those that would be associated with them by chance. These biological functions are found on bioinformatics annotated databases such as The Gene Ontology or KEGG; the more abundant pathways are identified through statistical techniques such as Fisher’s exact test. All PEA tools require a list of genes as input. A few tools, however, read lists of genomic regions as input rather than lists of genes, and first associate these chromosome regions with their corresponding genes. These tools perform a procedure called genomic regions enrichment analysis, which can be useful for detecting the biological pathways related to a set of chromosome regions. In this brief survey, we analyze six tools for genomic regions enrichment analysis (BEHST, g:Profiler g:GOSt, GREAT, LOLA, Poly-Enrich, and ReactomePA), outlining and comparing their main features. Our comparison results indicate that the inclusion of data for regulatory elements, such as ChIP-seq, is common among these tools and could therefore improve the enrichment analysis results. |
| first_indexed | 2024-04-12T01:17:12Z |
| format | Article |
| id | doaj.art-0292712e81a34c6d9f1fe4ec4c49aa4b |
| institution | Directory Open Access Journal |
| issn | 2673-7647 |
| language | English |
| last_indexed | 2024-04-12T01:17:12Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioinformatics |
| spelling | doaj.art-0292712e81a34c6d9f1fe4ec4c49aa4b2022-12-22T03:53:54ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472022-10-01210.3389/fbinf.2022.968327968327A brief survey of tools for genomic regions enrichment analysisDavide Chicco0Giuseppe Jurman1Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, ON, CanadaData Science for Health Unit, Fondazione Bruno Kessler, Trento, ItalyFunctional enrichment analysis or pathway enrichment analysis (PEA) is a bioinformatics technique which identifies the most over-represented biological pathways in a list of genes compared to those that would be associated with them by chance. These biological functions are found on bioinformatics annotated databases such as The Gene Ontology or KEGG; the more abundant pathways are identified through statistical techniques such as Fisher’s exact test. All PEA tools require a list of genes as input. A few tools, however, read lists of genomic regions as input rather than lists of genes, and first associate these chromosome regions with their corresponding genes. These tools perform a procedure called genomic regions enrichment analysis, which can be useful for detecting the biological pathways related to a set of chromosome regions. In this brief survey, we analyze six tools for genomic regions enrichment analysis (BEHST, g:Profiler g:GOSt, GREAT, LOLA, Poly-Enrich, and ReactomePA), outlining and comparing their main features. Our comparison results indicate that the inclusion of data for regulatory elements, such as ChIP-seq, is common among these tools and could therefore improve the enrichment analysis results.https://www.frontiersin.org/articles/10.3389/fbinf.2022.968327/fullgenomic regions enrichment analysispathway enrichment analysesfunctional annotationsfunctional enrichment analysisbioinformatics |
| spellingShingle | Davide Chicco Giuseppe Jurman A brief survey of tools for genomic regions enrichment analysis Frontiers in Bioinformatics genomic regions enrichment analysis pathway enrichment analyses functional annotations functional enrichment analysis bioinformatics |
| title | A brief survey of tools for genomic regions enrichment analysis |
| title_full | A brief survey of tools for genomic regions enrichment analysis |
| title_fullStr | A brief survey of tools for genomic regions enrichment analysis |
| title_full_unstemmed | A brief survey of tools for genomic regions enrichment analysis |
| title_short | A brief survey of tools for genomic regions enrichment analysis |
| title_sort | brief survey of tools for genomic regions enrichment analysis |
| topic | genomic regions enrichment analysis pathway enrichment analyses functional annotations functional enrichment analysis bioinformatics |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2022.968327/full |
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